The increase, at constant cost, in computer calculation power, is permitting the development on an industrial scale of automatic monitoring means intended especially to assist operators responsible for visual monitoring. These means call greatly upon image processing techniques whose performance, in terms of speed of analysis and definition, depends greatly on the calculation power used.
The methods employed to perform such processing consist, as a general rule, in comparing a two-dimensional or preferably three-dimensional image of the surface of the tire to be inspected with a two-dimensional or preferably three-dimensional reference image of the surface of the said tire. For this purpose, one seeks to match the image or the surface of the tire to be inspected and the image or the reference surface, for example, by superimposing them, and manufacturing anomalies are determined by analysing the differences between the two images or the two surfaces.
However, these methods do not allow the detection of surface defects that have no noticeable impact on the geometry of the said surface.
Hence, manufacturers are endeavouring to develop image analysis methods, complementary to the methods mentioned hereinabove, that are able to pick out anomalies present on the surface of the casing. These anomalies, whose dimensions are small, are manifested by a particular coloration, an abnormal shape, or else a particular and unusual spatial distribution, and are embedded in the global image of the tire surface, in which they may merge into one. Moreover, their occurrence is random on the surface of a tire or from one tire to another. This gives rise to a paucity of meaningful information making it possible to determine numerical protocols.
Thus, publication EP 2 034 268 calls upon the technique of wavelets to detect repetitive structures such as ply cords apparent on the interior surface of tires.
Publication EP 2 077 442 proposes a method for selecting filters that are able to digitally process the image of the surface of a tire and which are sensitive to a particular defect. This method for selecting filters calls upon the known procedures of texture analysis, and proposes to select the filters by a so-called genetic selection method. This method consists in varying in a statistical manner a collection of filters chosen beforehand, and in measuring with the aid of a cost function, the sensitivity of this modification on the detection of a defect identified beforehand, with respect to the initial collection of filters.
This method, which requires a lengthy and expensive training phase, presents the drawback, however, of not giving certainties about the convergence of these successive iterations on the one hand, and about the elimination of local optimums on the other hand.